1 citations · 1 across the 15 of their papers we have counts for
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Multiscale Graph Construction Using Non-local Cluster Features
Reina Kaneko, Hayate Kojima, Kenta Yanagiya +3
This paper presents a multiscale graph construction method using both graph and signal features. Multiscale graph is a hierarchical representation of the graph, where a node at eac…
Time-Varying Graph Signal Estimation among Multiple Sub-Networks
Tsutahiro Fukuhara, Junya Hara, Hiroshi Higashi +1
This paper presents an estimation method for time-varying graph signals among multiple sub-networks. In many sensor networks, signals observed are associated with nodes (i.e., sens…
Efficient Learning of Balanced Signed Graphs via Iterative Linear Programming
Haruki Yokota, Hiroshi Higashi, Yuichi Tanaka +1
Signed graphs are equipped with both positive and negative edge weights, encoding pairwise correlations as well as anti-correlations in data. A balanced signed graph has no cycles…
Constructing an Interpretable Deep Denoiser by Unrolling Graph Laplacian Regularizer
Seyed Alireza Hosseini, Tam Thuc Do, Gene Cheung +1
An image denoiser can be used for a wide range of restoration problems via the Plug-and-Play (PnP) architecture. In this paper, we propose a general framework to build an interpret…
PHISWID: Physics-Inspired Underwater Image Dataset Synthesized from RGB-D Images
Reina Kaneko, Takumi Ueda, Hiroshi Higashi +1
This paper introduces the physics-inspired synthesized underwater image dataset (PHISWID), a dataset tailored for enhancing underwater image processing through physics-inspired ima…
Lossy Compression of Adjacency Matrices by Graph Filter Banks
Kenta Yanagiya, Junya Hara, Hiroshi Higashi +2
This paper proposes a compression framework for adjacency matrices of weighted graphs based on graph filter banks. Adjacency matrices are widely used mathematical representations o…
Optimizing in NN Graphs with Graph Learning Perspective
Asuka Tamaru, Junya Hara, Hiroshi Higashi +2
In this paper, we propose a method, based on graph signal processing, to optimize the choice of in -nearest neighbor graphs (NNGs). NN is one of the most popular appro…